Climate impacts on agriculture in West Africa

Transcription

1 Benjamin SULTAN LOCEAN : Laboratoire d Océanographie et de Climatologie par l Expérimentation et l Approche Numérique Climate impacts on agriculture in West Africa 1

2 The world s largest rainfall deficit of the last century 2

3 Impacts on Water resources Human activities Health 3

4 An illustration of hydrological impacts 4

5 Sahel region (last century) Wet I Dry I I I Niger at Malanville: km² I A decrease of 50 % of the Niger runoff 5

6 An illustration of health impacts: malaria and meningitis 6

7 Meningitis and climate in West Africa Every year Western African countries within the Sahelo-Sudanian band are suffering important meningococcal meningitis disease outbreaks Affect up to 200,000 people, from which mainly young children Interaction between different environmental parameters (e.g. immune receptivity of individuals, a poor socio-economical level, the transmission of a more virulent serotype, social habits like pilgrinages, tribe migrations and meetings, and some specific climatic conditions) may intervene in disease outbreaks and diffusion within local populations The role of climate: The timing of the epidemic year, which starts in February and ends late May the spatial distribution of disease cases throughout the Meningitis Belt 7

8 Meningitis and climate in West Africa This Sahelo-Sudanian region is submitted to sequence of dry winter, dominated by Northern winds, called the Harmattan, and wet season starting at spring with the monsoon. The winter characteristics, through a weakening of human mucous membranes of the oral cavity due to air dryness and strong dust winds, make propicious conditions to the development of the meningoccus bacteria Humidity during both the Spring and Summer seasons strongly reduce disease risk due to 8 lower transmission capacity by the bacteria

9 Malaria in West Africa Malaria is caused by a parasite called Plasmodium, which is transmitted via the bites of infected mosquitoes. The vector anopheles In West Africa, malaria is involved in 90% of the mortality of children younger than 5 years old. 9

10 A map of malaria based on climate factors The three main climate factors that affect malaria are temperature, precipitation, and relative humidity (Pampana, 1969). Climate predicts, to a large degree, the natural distribution of malaria (Bouma and van der Kaay, 1996). 10

11 Climate and malaria Climate suitability for malaria in South Africa Number of cases of malaria in South Africa 11

12 Climate impacts on agriculture Climate has a strong influence on agricultural production : The most weather-dependent of all human activities (Oram 1989; Hansen 2002) Socio-economical impacts whose severity varies from one region to another (Ogallo et al. 2000) These impacts are particularly strong in developing countries in the tropics : - High variability in climate like the monsoon system over West Africa and India and the ENSO influence over the American continent (Challinor et al. 2003) - Poverty increases the risk and the impact of natural disasters (UNDP 2004). This is especially true in the Sahel : - Rainfed crop production is the main source of food and income - Means to control the crop environment are largely unavailable to farmers (no irrigation, low use of mechanization, fertilizers) - Rapidly growing population 12

13 Climate impacts on agriculture The relationships between crop and climate The scale issues The incertainties The potential benefits of climate forecasts 13

14 Agriculture in West Africa Population / 1000 Fraction of population in the agriculture sector (%) Contribution of agriculture in the GDP (%) 14

15 Two crop types in West Africa The food crops (ex : millet, sorghum) The cash crops (ex : maize, cotton) 15

16 The temporal variations of climate play a role in agriculture: The seasonal scale 16

17 The seasonal cycle of rainfall in Senegal Rai infall (mm/month) j f m a m j j a s o n d Time Same quantity of rainfall in Senegal and in Paris but it follows a seasonal cycle This seasonal cycle is very important for agriculture 17

18 A millet field throughout the rainy season May-June (sowing) June-July (early stages) July (weeding) August-September (late stages) 18

19 The millet after the rainy season Pearl millet at maturity The flowering structure (inflorescence) in pearl millet is called as panicle or head. In Africa pearl millet is consumed as fermented and nonfermented flat breads, couscous, thick and thin porridges, boiled and steamed foods, and alcoholic and nonalcoholic beverages. 19

20 The temporal variations of climate play a role in agriculture: The interannual scale 20

21 Rainfall and yield in Niger Wet years Source : FAO, Agrhymet Dry years We find the same tendancy in rainfall and yield Climate drives yields and food supply 21

22 The spatial variations of climate play a role in agriculture 22

23 Rainfall in West Africa: climatology and tendancies 23

24 Potential of biomass production in West Africa: climatology and tendancies 24

25 Quantifying the relationships between climate and crop yield Crop modelling Simulate explicitly the growth of the plant, quantify water and other stresses that affect the development of the crop Advantage: takes into account stresses occurring during sensitive stages of the crop Disadvantage: requires many agronomic data to calibrate the crop model, requires precise climate data (daily time scale, plot scale) Statistical analyses For instance, linear regression between seasonal rainfall amount and yield Advantage: does not require many agronomic data or fine scale climate data Disadvantage: not sensitive to intraseasonal scales, needs large dataset to calibrate the relationships, stationnary

26 The statistical links between rainfall and crop yield Linear regressions between yield and rainfall 3000 Cotton in Mali Millet in Niger R² = 0,437 Predicted d yield (kg/ha) Observed yield (kg/ha) Observed yield (kg/ha) Rainfall (mm/year) Rendement simulé = F (length of the rainy season, water budget) R²=0.86 Données d un essai de longue durée conduit par l IER/SRCFJ (Section Recherche Cotonnière et Fibre Jutière de l Institut d Économie Rurale) au Mali de 1965 à 1990 dans la région de Koutiala (station de N'Tarla) Annual rainfall amount and crop yield in Niger R²=0.44 Données FAO en moyenne sur l ensemble du Niger sur la période

27 Groundnut yields and climate in India A large fraction of the world production of groundnut comes from India Not irrigated and thus highly dependant on climate Fluctuations in the Indian monsoon explain half the variability of the yields (52%) Challinor et al. (2003) 27

28 Groundnut yields and climate in India 28 Challinor et al. (2003)

29 Validation of a crop model in Senegal Simulated Yield (kg/ha) Observed Yield (kg/ha) Validation over a research station The model explains near 90% of the variability of yield From a research station to a farm the main factors controlling the yield change Observed Yield (kg/ha) Simulated Yield (kg/ha) On-farm validation The model simulates the attainable yield and under-estimates the onfarm variability Sultan et al. (2005) 29

30 The drivers of the simulated yields Baron et al. (2005) 30

31 The drivers of the simulated yields 31 Baron et al. (2005)

32 Climate impacts on agriculture The relationships between crop and climate The scale issues The incertainties The potential benefits of climate forecasts 32

33 Scale issues in modelling the impacts of climate Large scale Climate variability Space Fine scale Disaggregation Time IMPACTS Fine scale Aggregation Human activities 33

34 Regional climate local impact CLIMATE VARIABILITY Large scale TIME SPACE - Climate models -Long-term drought - Climate change Fine scale Downscaling If the models do a credible job on the global scale they fail on the regional scale: Subgrid-scale processes (cloud formation, rainfall, infiltration, evaporation, runoff, etc.) are parameterized and badly simulated Fine scale Yield at the plot level IMPACTS However, these subgrid processes are actually those with the greatest ecological or societal impact, since they strongly affect the local climate at the scales of the human and ecological environment. We need downscaling Find large scale patterns with an impact at the local scale 34

35 Regional climate local impact Statistics of annual mean responses to the SRES A1B scenario, for 2080 to 2099 relative to 1980 to 1999, calculated from the 21- member AR4 multi-model ensemble using the methodology of Räisänen (2001). 35 Source: GIEC 2007

36 Effect of aggregation on simulating yield Counts P=0.25 It rains almost every day (P=0.9) It never rains Rain event frequency Baron et al. (2005) It rains every day 36

37 Effect of aggregation on simulating yield 37

38 Effect of aggregation on simulating yield Baron et al. (2005) 38

39 Downscaling Global climate (GCM, NCEP ) Topography, land use, landsea distribution Regional specificities To combine large-scale information with regional specificities to simulate the local climate. Local climate Dynamical and statistical approaches 39

40 Downscaling Dynamical methods LAMs or RCMs are sophisticated atmospheric (or oceanic) models of a limited geographical area with a resolution of the order of km, that use the largescale fields simulated by the GCMs as boundary conditions, but that take the regional characteristics, such as topography, into account. 3 types of statistical methods Weather generators stochastic models generating virtual climate series with the same statistical properties than observed ones Regression-type methods canonical analyses, multiple regression, neural networks Weather types Analogues, clustering 40

41 Scale issues in modelling the impacts of climate Large scale Climate variability Space Fine scale Disaggregation Time IMPACTS Fine scale Aggregation Human activities 41

42 A multi-scale and multidisciplinary field survey in Niger Nb de parcelles Yield x 2 The plot scale Nb de parcelles Koyria Rendement Gardama Kouara Alkama Rendement Berkiawal 30 km Yield x 2 Wankama Banizoumbou The village scale Torodi Sadore (IH Jachere) Kare Tanaberi A multi-scale and multidisciplinary field survey in Niger

43 From the plot level to the village level Validation of the crop model at different aggregation-levels Observed Yield rd dt.new[which(cyclecomplet <= cycle)] R= Simulated Yield RDTsimule[which(Cyclecomplet <= cycle)] Plot level Observed Yield obs.mean Aggregation R= Simulated Yield sim.mean Village level At the village level, the mean yield is better predicted by the crop model We need upscaling 43

44 Climate impacts on agriculture The relationships between crop and climate The scale issues The incertainties The potential benefits of climate forecasts 44

45 Predicting Climate impacts on agriculture The prediction and scenarios of climate impacts: - Seasonal prediction (prediction of yield from one year to another) - long-term scenarios (climate change) Many uncertainties in such forecast and scenarios due both to climate predictability and to the vegetation response 45

46 Uncertainties in climate change impacts on agriculture 46

47 Uncertainties in climate change impacts on agriculture perc centage of studies 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Tropical Cultures tropicales crops Temperate Cultures tempérées crops Positive Null Negative Impacts of climate change on crop yields based on the analysis of 43 studies mentionned in the IPCC

48 Uncertainties in climate change impacts on agriculture 48

49 Change in T and rainfall Reference period: Dots: the difference in the multi-models mean is greater than the inter-model standard deviation 49 Source: GIEC 2007

50 Période de référence: Figure Dots: More than 80% of the models agree in the sign of the change 50 Source: GIEC 2007

51 Performance of the models in simulating the seasonal cycle of rainfall in West Africa D Orgeval et al. (2005) 51

52 The performance of the models in simulating the WAM in the present is not the only factor to explain uncertainties in the future projections A selection of the best three models in the simulation of present climate in West Africa: The uncertainty remains! Sahelian JJAS precipitation differences (mm/day) from the mean in various GCM simulations with A2 scenario forcing after Source: Cook and Vizy

53 Uncertainties in climate change impacts on agriculture The potential yield is almost never attained because of limiting factors and reducing factors To anticipate the response of yield to climate change, we need to know the evolution of each factor, their interaction and their impact on the crop Crop production levels depending on defining, limiting or reducing factors (see [Goudriaan and Zadoks, 1995]). Furher. (2003) 53

54 The fertilizer role of CO2 for the crops Predicted effect of CC 2050 on crop yield (Parry et al. 2004) Effect of CO2 on the Leaf Photosynthesis 54

55 Combination of several factors The different factors interact in a non-linear way Effects of elevated CO2 and increased temperature, singly and in combination, on yield of wheat. The data represent the ratio of yield relative to current ambient CO2 and temperature (relative yield change). Data are taken from the review by [Amthor, 2001], Table 7). Plots show median and standard percentiles (n=17). Furher (2003) 55

56 How to represent uncertainty using GCMs? 56 Palmer et al. (2004)

57 The multi-models approach 57 Palmer et al. (2004)

58 The multi-models approach GLAM with YGP calibrated using : The respective singlemodel yield ensemble mean ERA40 The multi-model ensemble mean For a given GCM, the forecast error is weaker by using an ensemble of simulations (true for most of the used models) By using the ensemble multi-model mean, we reduce the forecast error (only one model has a weaker forecast error than the MMEM) Challinor et al. (2005) 58

59 The relative contributions to uncertainties Maize yields projection under future climate in China Measuring the uncertainties linked to biophysical processes (sensitivity with 60 parameters in crop modelling) and to climate scenarios (sensitivity with 10 scenarios) Comparison between probability density functions of projected yield changes during 2050s based on different number of climate change scenarios and sets of parameters across the maize cultivation grids in Shandong Palmer et al. (2004) 59

60 Climate impacts on agriculture The relationships between crop and climate The scale issues The incertainties The potential benefits of climate forecasts 60

61 The potential benefits of climate forecasts to agriculture Climate predictability Potential to benefit Human vulnerability The opportunity of a beneficial use of climate forecasts falls within the intersection of: - Human vulnerability - Climate predictability - Decision capacity Decision capacity 5 Prerequisites to beneficial forecast use: Forecast information must address a need real and perceived Existence of viable decision options sensitive to forecast information Prediction in relevant periods, at an appropriate scale, with sufficient accuracy and lead-time for relevant decisions Effective communication of relevant information Institutional commitment and favorable policies Hansen (2002) 61

62 Forecast information must address a need real and perceived 62

63 Forecast for July-September 2007 Forum PRESAO ACMAD May 2007 Probabilistic forecast 63

64 The needs of farmers (1) A questionnaire addressed to commercial farmers in South-Africa : Among the factors influencing decisions (such as market prices and tendancies ), climate forecast is an important factor (seasonal rainfall, start of the rainy season, risk of drought ). To deliver a relevant service climate forecast should consider the critical times for farming activities (i.e. planting) and for plant growth (i.e. grain-filling period) Klopper et al. (2006) 64

65 The needs of farmers (2) In Burkina Faso, most farmers expressed strong interest in receiving seasonal rainfall forecast The most salient rainfall parameters farmers in Burkina Faso want in a forecast (in order of declining priority): Onset and end of the rainy season Rainfall distribution within the rainy season Total amount of rainfall At the moment, only the total amount of rainfall is forecasted in West Africa 65 Ingram et al. (2002)

66 Rainfall and millet yields in Niger The total rainfall amount does not explain the variability of the yields 250 The role of intraseasonal variability Late onset Early cessation Mai Juin Juillet Août Septembre 66

67 Existence of viable decision options sensitive to forecast information 67

68 Decision options sensitive to forecast informations (1) Grain Yield (kg/ha) Millet Sorghum Maize Farmers' Fields Research Stations Yield gap A yield gap between research stations yield (and simulated yield) and on farm yield This yield gap increases with rainfall Yield gap Rainfall during rainy season Relationship between rainfall during the rainy season and yield of maize, sorghum and millet at 15 dryland locations in India (after Shivakumar et al. 1983) Fertilizers are more useful in good rainfall years Prediction of good rainfall years is thus useful for farmers 68

69 Decision options sensitive to forecast informations (2) Potential response strategies in response to rainfall forecast in Burkina Faso Above Normal Below normal Month requir. Clear upland areas for planting Order less herbicide Jan Order less insecticide Sell livestock or go in transhumance Jan Plant longer duration crops/varieties Plant shorter duration crops/varieties Feb Plant more cash crops Plant more cereal crops May Apply more fertilizer or manure Apply less fertilizer or manure Jun Agricultural responses Sell grain stocks during rainy season Store grain stocks Jul Acquire capital to purchase inputs Ration food Jan Increase income-generating enterprises Jan Migrate Mar Purchase or borrow food grain Apr Send younger men abroad to work Jun (after Ingram et al. 2002) Non-agricultural responses 69

70 Prediction in relevant periods, at an appropriate scale, with sufficient accuracy and lead-time for relevant decisions 70

71 Intraseasonal fluctuations of rainfall in Sahel Dry spell at 15 days Dry spell at 40 days Dry spell at 15 days Intraseasonal fluctuations of rainfall at two different timescales: Around 15 days Around 40 days 71

72 The role of intraseasonal variability of rainfall The link between yield and rainfall during the critical stages of the crop growth An important role of rainfall during the critical stages (R=0.51) 72

73 Importance of the forecast timing Farmers from US (Mjeld et al. 1988) and from Burkina Faso (Ingram et al. 2002) agree : A less accurate forecast with a sufficient lead-time is more valuable than a highly accurate forecast that arrives after farmers have made irrevocable decisions Lead-time 3-4 months before Strategic decisions Clearing new fields, applying more and more manure, ordering inputs 1-2 months before onset of the rains Optimize labor and land allocation, obtain seed of different varieties, prepare fields in different locations Though a less value, forecasts could still contribute to revisions of minor farm decisions (Ingram et al. 2002) 73

74 Quantifying the potential benefits of climate forecasts Advanced information in the form of seasonal climate forecasts has the potential to improve farmers decision making, leading to increases in farm profits. Because seasonal climate forecasts may have an impact on farmers welfare, both qualitative and quantitative assessments are important to fully exploit the potential benefits associated with them (value) and to understand the limitations of their application (use). Two methods: Ex-ante evaluation and Ex-post evaluation Ex-ante valuation seeks to assess the potential benefits of an innovation in advance of its adoption, while ex-post valuation seeks to assess actual outcomes following adoption. 74

75 Quantifying the potential benefits of climate forecasts Although seasonal forecasts have been issued routinely for more than two decades in parts of the world, their effective dissemination and systematic use to manage climate risk in agriculture represent a new innovation relative to most other agricultural technologies in most cases too new for reliable ex-post assessment of value. Ex-ante assessment of the value of seasonal forecasts serves two related roles (Thornton 2006): it provides the evidence needed to mobilize funds and influence the agendas of institutional partners in the face of competing priorities. it provides insights that inform targeting of effort (e.g., farming systems, locations, forecast characteristics, and decision support tools) where the net benefits are likely to be greatest. 75

76 Evaluating the potential benefits of climate prediction Skillful climate forecasts : «the next green revolution» or inability to use the climate informations???? For nine test years, seasonal rainfall forecasts are provided to 7 commercial farmers. They had then to indicate the impacts on yield either positive or negative of reacting to the forecasts. In 1994, more than 50% of the farmers would have benefited from having seasonal forecasts No benefits (60%) Benefits (33%) Negative impacts (6%) 76 After Klopper et al. (2006)

77 Quantifying the potential benefits of climate forecasts In West Africa: very few studies despite the importance of agriculture in Sahelian countries This quantification is difficult because of the interactions of several factors: dessimination and effective use of the forecasts, available decisions options sensitive to the forecasts An ex-ante study to give some quantitative elements on the potential benefits of climate forecasts for agriculture in West Africa 77

78 Methodology Bioeconomic model A very simple model to simulate farmers decision with a priori information on the quality of the rainy season Sensitivity to the forecasts skill Is the economical value of the forecasts very sensitive to the forecasts skill? Evaluation of existing forecasts schemes Do existing forecasts schemes (DEMETER, PRESAO) have an economic value? 78

79 Rainfall (Hulme) DEMETER (7 x 9 runs) SST (Reynolds) JAS

80 Farm characteristic 3 workers 6 persons Family consumption 200 kilos of grains Land type: lowland, deck and dior FCFA of capital 4 crops (millet, sorghum, mais, peanuts), 3 intensification levels for maize 1 hectare manured with animal dung 80

81 Model GAMS (General Algebraic Modeling System) Maximize the expected income according to: Prices, yields, inputs prices Three types of rainy seasons land, labor, capital and food security constraints 81

82 What is the economic value of using seasonal forecasts? 82

83 Model s optimal crop allocation according to the quality of the rainy season 4,5 4 Hectares 3,5 3 2,5 2 1,5 1 0,5 0 a0 a1 a2 a3 CTRL DRY NORM WET sorgh,baf mil,dior mil,deck mais2,deck arac2,dior arac1,dior arac1,deck 83

84 Costs and benefits of seasonal forecasts Benefits Benefits (%) Impacts DRY forecasts WET forecasts DRY years NORM years WET years Success Failure Failure Success 84

85 Gross margins Gross margins (FCFA/ha) a1 a2 a3 Standard deviation (kg/ha) arac1* arac2* mais1 mais2 mais3 mil sorgho Standard deviation arac1 arac2 mais1 mais2 mais3 mil sorgho The WET strategy is the riskier with the choice of Maize and Groundnut 85

86 Which sensitivity of the benefits to the forecasts skills? 86

87 Forecasts evaluation matrix OBSERVATION DRY NORM WET PREDICTION DRY A B C NORM D E F WET G H I HR dry = A / (A+D+G) HR wet = I / (C+F+I) 87

88 Forecast = Rainfall + Noise * k If k = 0, COR=1, HR=1 : perfect forecast COR and HR decrease with an increase of k 88

89 Use of dry years forecasts Use of wet years forecasts 89

90 Use of dry years forecasts Use of wet years forecasts 90

91 Do existing forecasts schemes (DEMETER, PRESAO) have an economic value? 91

92 Assessing the economic value of 3 widely known prediction schemes (deterministic forecasts) Persistence based predictions Statistical SST-based predictions (PRESAO-like) DEMETER dynamical forecasts 92

93 Forecast skills and benefits HR dry COR Benefits SST June SST May SST April Persistence HR dry COR Benefits DEMETER DEMETER corr

94 Use of dry years forecasts Use of wet years forecasts 94

95 Use of dry years forecasts Use of wet years forecasts 95

96 Conclusions Studying the impacts of climate on agriculture is a very important issue in West Africa (populations highly vulnerable to fluctuations in the agricultural sector) But it raises many issues Scale incompatibility between climate and agriculture Bad representation of crucial variables Uncertainty in climate and yield projections Trying to reduce this uncertainty is a key point to produce useful information for stakeholders 96

97 Références Baron C., B. Sultan, M. Balme, B. Sarr, T. Lebel, S. Janicot and M. Dingkuhn, From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact, Phil. Trans. Roy. Soc. B, 360 (1463), Bazzaz, F. and W. Sombroek, 1996 : Global climate change and agricultural production. Direct and indirect effects of changing hydrological, pedological and plant physiological processes. John Wiley, FAO, Rome, Italy. Challinor, A.J., J.M. Slingo, T.R. Wheeler, P.Q. Craufurd and D.I.F. Grimes, Toward a combined seasonal weather and crop productivity forecasting system : determination of the working spatial scale. J. Appl. Meteorol., 42, Challinor, A.J., T.R. Wheeler, P.Q. Craufurd, J.M. Slingo, and D.I.F. Grimes, Design and optimisation of a large-area process-based model for annual crops. Agric. For. Meteorol., in press. De Rouw, A., Improving yields and reducing risks in pearl millet farming in the African Sahel. Agricultural Systems, 81, Dingkuhn M., B.B. Singh, B. Clerget, J. Chantereau and B. Sultan, Past, present and future criteria to breed crops for water-limited environments in West Africa, Agricultural Water Management, 80 (1-3), Hansen, J.W., Realizing the potential benefits of climate prediction to agriculture 97: issues, approaches, challenges. Agricultural Systems, 74,

98 Références Ingram, K.T., M.C. Roncoli, and P.H. Kirshen, Opportunities and constraints for farmers of West Africa to use seasonal precipitation forecasts with Burkina Faso as a case study. Agricultural Systems, 74, Ogallo, L.A., M.S. Boulahya, and T. Keane, Applications of seasonal to interannual climate prediction in agricultural planning and operations. Agric. For. Meteorol., 103, Sivakumar, M.V.K., 1988 : Predicting rainy season potential from the onset of rains in Southern Sahelian and Sudanian climatic zones of West Africa, Agricult. And Forest. Meteorol., 42, Sultan, B. and S. Janicot, The West African monsoon dynamics. Part II: The pre-onset and the onset of the summer monsoon. J. Climate, 16, Sultan, B., C. Baron, M. Dingkuhn and S. Janicot, Agricultural impacts of largescale variability of the West African monsoon. Agric. For. Meteorol. 128, UNDP, Reducing disaster risk : a challenge for development, UNDP global report, Pelling M. (Ed.), 146 pp. Zorita, E. and H. von Storch, The analog method - a simple statistical downscaling technique: comparison with more complicated methods. J. Climate, 12,